Responsibilities:

• Be part of a team working on building out scalable infrastructure to train, evaluate, deploy, perform inference, and monitor our ML models
• Build, deploy, and maintain generative AI services & applications
• Create data systems to collect, clean, label, and store data used for model features
• Deploy and manage various applications in our Kubernetes clusters
• Collaborate with Machine Learning engineers to build & support state-of-the-art experimentation platforms, training framework,s and associated tools
• Work with stakeholders on requirements and solutions for ML infrastructure
• And of course, you will be coding every day!

Requirements & Skills:

• 5+ years of industry experience building production-level ML platforms and infrastructure, including experience building ML systems/pipelines from the ground up.
• Ability to write high-quality code in Python, Java, or Scala
• Experience building production-ready RESTful APIs, as well as having scaled platforms in production to a large number of users.
• A desire to own large parts of an ML Platform, with a strong understanding of ML models & principles.
• Experience working with containers and deploying applications to Kubernetes
• Experience with LLMs and building infrastructure to support LLM applications
• Experience with relational and low-latency databases
• Experience with transforming data in both batch and streaming contexts
• A desire to learn new technologies quickly, and a proven track record of making quality vs. deadline tradeoffs in fast-paced environments.
• Ability to scope out a large project and manage it through project delivery
• Strong communication skills and ability to generate consensus and buy-in within the team
• Organizational skills and the ability to simplify complex problems and prioritize what matters most for the sake of the team and the business
• Experience working with highly sensitive data in a healthcare environment
• Experience working with ML frameworks such as PyTorch, SciKit-learn, XGboost
• Experience working with ML Ops tools such as MLFlow, Kubeflow, AWS Sagemaker
• Experience building solutions on cloud infrastructure, particularly AWS

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Responsibilities:

• Delivering AI/ML models from initial planning to data sourcing and preparation, building and training models, and deploying models to production in our cloud environment, ideally using and deploying open-source models
• Creating AI-based prototypes to evaluate model and design options, working with internal stakeholders to analyze the quality and relevance of results
• Training and tuning models to optimize outcomes of algorithms
• Integrating models into production, working with Engineering team members as needed to integrate models into product architecture
• Working with internal stakeholders to understand the product needs of AI and to scope potential solutions
• Conducting research into relevant AI models and technologies and staying well-connected to trends and developments in this rapidly evolving space

Requirements & Skills:

• 5+ years of software development experience with at least 3 years of experience implementing ML and AI technologies
• Deep knowledge of data modeling, and current ML and AI technologies with experience working with LLMs in particular to develop language-based solutions
• Highly analytical with the ability to think outside of the box, consider potential solutions from many different sources, and make decisions with rapidly evolving or ambiguous data points
• Creative and scrappy problem solver who is excited about the prospect of creating greenfield solutions using new and evolving technologies
• Strong written and verbal communication skills with the ability to present complex ideas
• Highly collaborative with the ability to work cross-functionally to understand needs and execute against ideas
• Thrives in a startup environment where speed, agility, and high performance are table stakes

The post Machine Learning Engineer, ProNavigator appeared first on AI Jobs Latest.

Responsibilities:

• Develop a long-term strategy and roadmap for integrating AI/ML technology across our product portfolio.
• Lead the development of core AI/ML models and algorithms that drive our product’s key use cases in the cybersecurity and observability domains.
• Partner with Product Leads to build a strategic product vision coupled with a flexible science/engineering plan, supporting learning through iteration.
• Stay up to date with the latest research and developments in the field of AI/ML, and ensure that these advancements are properly incorporated into our technology roadmap.
• Provide technical guidance and mentorship to team members to ensure the development of their skills.
• Develop structures to hold the team accountable for progress, learning, communicating, and meeting business objectives.

Requirements & Skills:

• In-depth knowledge and proven track record in AI/ML technology, including deep learning, time series modeling, natural language processing, and unsupervised learning.
• In-depth knowledge and proven track record in Generative AI technology and Large Language Models (LLM).
• Experience with leading and managing machine learning teams of applied scientists and engineers.
• Experience with developing large-scale, complex models and deploying them in production systems.
• Experience with setting and executing innovation strategies at both technical and organizational levels to drive business objectives.
• Familiarity with architectural patterns of large, high-scale applications.
• Experience in the Cybersecurity industry is preferred.

The post AI Engineering Manager, Splunk appeared first on AI Jobs Latest.

Responsibilities:

• Participate in team tasks and provide technical leadership to the team. Including, but not limited to writing robust code with comprehensive tests, code reviews, code deployments, data maintenance tasks, architecture discussions, and design, and mentorship. 
• Keep updated with the latest technologies in ML/NLP/AI and write production-level code to apply document understanding algorithms/models developed internally and from the community to solve our business problems. 
• Actively participate in the complete data science lifecycle, from business understanding and problem framing, model development and integration, to model deployment and quality monitoring in production. 
• Work closely and professionally with different stakeholders to identify opportunities for the development of new data extraction pipelines or improvement in existing pipelines. 
• Contribute to projects, adhere to project timelines, and deliver solutions on time. 
• Assist with technical incidents by participating in troubleshooting and problem-solving activities, post-incident discussions/analysis, precaution implementation, etc. 
• Assist as needed with technical reviews of code samples and in technical interviews. 
• When being in a technical lead role, collaborate with teams outside of Engineering to ensure that all stakeholders understand project goals, requirements, and realistic timelines. 

Requirements & Skills:

• 5+ years of hands-on experience with machine learning/natural language processing/large language models. 
• Bachelor’s degree in computer science/engineering discipline or equivalent experience. 
• Strong programming skills in Python, and SQL. 
• Familiar with modern ML/LLM tools, such as TensorFlow, PyTorch, Hugging Face, RAG, LangChain, etc. 
• Excellent debugging, troubleshooting, and problem-solving skills. 
• Knows how to structure applications for long-term maintenance and ongoing flexibility. 
• Skills in designing scalable systems. Understands common architecture technologies such as message queues. 
• Great communication skills (written and verbal) and be a fast learner. 
• Experience writing and reading RESTful APIs. 
• Database experience such as PostgreSQL. 
• Experience communicating directly with customers to synthesize their requirements and align them with business goals. 

Good to Have

• Expertise in document understanding, information retrieval, PDF document parsing, or a related domain. 
• Experience working with cloud technology stack (e.g. AWS). 
• Experience in LLM agentic design patterns. 
• Knowledge of the medical insurance industry and the ability to speak directly to medical professionals. 
• Knowledge of distributed design patterns is an asset. 
• Mentored developers to raise the quality of the organization.

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Responsibilities:

• Developing complex applications leveraging API-based LLMs
• Training and deploying modern machine learning pipelines
• Experience in data science methodologies and technologies
• Computer security and security research
• Experience in Site Reliability Engineering (SRE) or other operational roles
• Experience in Spark, Databricks, Weights and Biases, Airflow, MLFlow, or other MLOps/AIOps technologies
• Modern Python libraries, coding practices, and tooling (asyncio, pydantic, pandas, ruff, mypy)
• Experience with GPU technologies and compute frameworks (Morpheus, RAPIDS, JAX, Pytorch, CUDA)
• Familiarity with Google Cloud Platform and/or Microsoft Azure

Requirements & Skills:

• A degree in computer science or software engineering. Additional or directly relevant experience will be considered in lieu of a degree.
• 5+ years of experience in Python development and shipping Python code in production environments
• 3+ years of experience solving complex problems using modern AI/ML techniques
• Experience in software engineering and with data structures and algorithms
• Comfortable working cross-functionally across both research and product teams
• Experience with large-scale high-load distributed systems
• Excellent communication skills and ability to work asynchronously
• Experience with Docker, Kubernetes, Terraform, ArgoCD, or other similar technologies
• Familiarity with Amazon Web Services

The post Senior AI Platform Engineer – LLMs, SentinelOne appeared first on AI Jobs Latest.

Responsibilities:

• Work with and lead a team of data scientists and data engineers on defining and creating scalable AI data architecture strategy
• Identify risks and constraints of AI infrastructure at scale and implement mitigations and workarounds
• Create project plan, milestones and lead the team to Implement the AI data architecture strategy
• Lead the design and implementation of data access controls to produce a data transformation pipeline that discovers, repairs, logs, and deploys quality data plus security policy metadata
• Work with a team to design, build, train, and evaluate machine learning models
• Be the liaison between executive level leadership, research team and other stakeholders across Leidos.
• Communicate results and methods for solutions to internal and external stakeholders.
• Work within teams of AI/ML researchers and engineers using Agile development processes

Requirements & Skills:

• Bachelor’s degree in Computer Science, Data Science or related field and over 16  years of relevant experience, Masters with 14 years experience, or PhD with 10 years experience.
• Experience with data architecture at scale and AI Infrastructure
• Experience with data categorization, labeling and tagging
• Experience with data repositories and ETL
• Experience with Data Operations including applying AI for data curation
• Strong Python programming fundamentals
• Good understanding of machine learning algorithms, tools and platforms
• Experience with AI/ML tools, such as common Python packages (e.g., scikit-learn, NumPy, Pandas)
• Experience with tabular data analysis using languages such as SQL, R, and/or Python
• Experience with statistical modeling and data analysis
• Experience with leading research projects and project teams
• Experience with planning and execution
• Great communication skills, able to explain model results to a non-technical audience
• Proficient in data exploration techniques and tools such as Amazon Web Services (AWS)
• Experience with data visualization libraries such as Plotly, Streamlit, and matplotlib.
• Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, TensorBoard
• Experience with building LLM and other Generative AI applications.
• Willing to learn new skills and platforms to support data analytics.
• Ability and willingness to obtain a Top Secret security clearance

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